Genome Wide Association Coordinating Center

全基因组协会协调中心

• 批准号: 8326854
• 负责人: Bruce S. Weir
• 金额: $ 27万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-06 至 2012-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8326854
• 关键词: Back; Biometry; Biotechnology; Case-Control Studies; Clinical; Clinical Trials; Collaborations; Communication; Consult; Data; Data Analyses; Data Quality; Data Set; Databases; Development; Directories; Disease; Documentation; Environment; Environmental Exposure; Epidemiologist; Evaluation; Gene Components; Genes; Genetic Predisposition to Disease; Genotype; Goals; Group Meetings; Human; Individual; Information Distribution; Institution; Intervention Studies; Leadership; Manuscripts; Maps; Measurement; Methodology; Methods; Modeling; Nature; Phase; Phenotype; Population; Preparation; Procedures; Production; Public Health; Published Directory; Reporting; Research; Research Design; Research Personnel; Resources; Sampling; Sampling Studies; Site; Source; Structure; Technology; Time; United States National Institutes of Health; Universities; Washington; case control; cohort; data integration; data sharing; design; development policy; experience; flexibility; follow-up; genome wide association study; meetings; programs; symposium; tool; web site; working group

DESCRIPTION (provided by applicant): The NIH-wide Genes and Environment Initiative (GEI), developed to support efforts for identification of major genetic susceptibility factors for high impact diseases and potential causative environmental exposures, recently launched the Genome-Wide Associations (GWA) component to support genome-wide association studies in both the initial discovery or replication phases. To support the complexities of such an ambitious effort, the Department of Biostatistics at the University of Washington has convened a team of experts to serve as the Coordinating Center (GWA CC) to provide the necessary organizational and statistical expertise for integration of data, development of methodologies and tools for both data harmonization and analyses, and for the administration of those tasks needed for a large multi-site scientific study of this nature. Specifically, the GWA CC will serve as a centralized resource to facilitate and support the activities of the GEI-GWA program and for GEI-supported replication and fine mapping activities, sequencing, and functional studies by (1) developing, harmonizing and providing documentation of phenotypic data from studies included in this initiative and to provide the storage and transfer of all datasets between the National Center for Biotechnology and study sites as needed; (2) contributing to the development of these strategies by providing statistical support for modeling and selecting options for replication and follow-up studies, selecting targets for sequencing and functional studies; developing statistical methodology for harmonization of data to be applied to the combined set of phenotypes, identifying samples for genotyping according to selected platforms; providing statistical support for further development of analytic methods; and supporting data analyses of the combined dataset to study investigators; (3) providing administration and coordination of all activities by facilitating study communications across all sites by developing and using tools designed to allow and encourage information exchange; coordinating all Steering Committee and working group meetings and conference calls to include distribution of pertinent materials and production of all interim and final reports for the Steering Committee, Project Office, and working groups, (4) administering all tasks necessary for development of policies, documentation of and sharing of data, and (5) supporting all other efforts requested by the Steering Committee or Project Office as needed for successful coordination of the GEI-GWA Study. Coordination of the GEI-GWA Study will be done in a spirit of collaboration using creative and flexible approaches while providing leadership in statistical methodology and approaches to project management. This proposal brings together a strong team of statistical geneticists, biostatisticians, epidemiologists, programmers, analysts and project management staff with many years of related experience to successfully accomplish the goals of the GEI-GWA.

描述（由申请人提供）：NIH范围的基因和环境倡议（GEI），旨在支持识别高影响疾病的主要遗传易感性因素和潜在的致病环境暴露，最近启动了全基因组范围的关联（GWA）组件，以支持最初发现或复制或复制阶段基因组关联研究。为了支持这种雄心勃勃的努力的复杂性，华盛顿大学的生物统计学系召集了一个专家团队，以作为协调中心（GWA CC）提供了必要的组织和统计专业知识，以整合方法，用于将数据协调和分析的方法和工具的开发以及对这些任务进行大型科学的管理，以实现这一自然研究。具体而言，GWA CC将作为集中资源，以促进和支持GEI-GWA计划的活动，以及（1）通过（1）通过（1）开发，协调和提供在此目录中包含所有数据的储存和转移的表型数据中心的表型数据的文档，并提供（1）通过（1）通过（1）通过（1）通过（1）通过（1）进行综合的数据中心和提供均需的库存，以提供GEI-GWA计划的活动，并为GEI支持的复制和精细的映射活动，测序和功能性研究提供了（1）。 （2）通过为建模和选择复制和后续研究的选项提供统计支持，为这些策略的发展做出贡献，选择测序和功能研究的目标；开发统计方法来统一数据，以应用于组合表型的组合集，并根据选定的平台识别用于基因分型的样本；为进一步发展分析方法提供统计支持；并支持合并数据集的数据分析以研究研究者； （3）通过开发和使用旨在允许和鼓励信息交换的工具来促进所有站点的研究通信，从而提供和协调所有活动；协调所有指导委员会和工作组会议和会议呼吁，包括分配相关材料以及为指导委员会，项目办公室和工作组的所有临时和最终报告，（4）管理政策制定，数据和共享数据的文档和共享的所有任务，以及（5）指导委员会或项目办公室要求的所有其他努力。 GEI-GWA研究的协调将以一种创造性和灵活的方法为精神进行，同时提供统计方法论和项目管理方法的领导力。该提案汇集了一个由统计遗传学家，生物统计学家，流行病学家，程序员，分析师和项目管理人员组成的强大团队，并拥有多年相关的经验，以成功地实现GEI-GWA的目标。

项目成果

Statistical significance in genetic association studies.

遗传关联研究中的统计意义。

• DOI: 10.25011/cim.v33i5.14351
• 发表时间: 2010
• 期刊: Clinical and investigative medicine. Medecine clinique et experimentale
• 作者: Qu,Hui-Qi;Tien,Matthew;Polychronakos,Constantin
• 通讯作者: Polychronakos,Constantin

Association Between Human Leukocyte Antigen Class I and II Diversity and Non-virus-associated Solid Tumors.

• DOI: 10.3389/fgene.2021.675860
• 发表时间: 2021
• 期刊: Frontiers in genetics
• 影响因子: 3.7
• 作者: Liu Z;Hildesheim A
• 通讯作者: Hildesheim A

Evaluation of genetic association of the INK4 locus with primary open angle glaucoma in East Indian population.

INK4 基因座与东印度人群原发性开角型青光眼遗传关联的评估。

• DOI: 10.1038/srep05115
• 发表时间: 2014
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Vishal,Mansi;Sharma,Anchal;Kaurani,Lalit;Chakraborty,Subhadip;Ray,Jharna;Sen,Abhijit;Mukhopadhyay,Arijit;Ray,Kunal
• 通讯作者: Ray,Kunal

Bayesian mixture modeling of gene-environment and gene-gene interactions.

• DOI: 10.1002/gepi.20429
• 发表时间: 2010-01
• 期刊: GENETIC EPIDEMIOLOGY
• 影响因子: 2.1
• 作者: Wakefield, Jon;De Vocht, Frank;Hung, Rayjean J.
• 通讯作者: Hung, Rayjean J.

Imputation Accuracy Across Global Human Populations.

全球人口的估算准确性。

• DOI: 10.1101/2023.05.22.541241
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Cahoon,JordanL;Rui,Xinyue;Tang,Echo;Simons,Christopher;Langie,Jalen;Chen,Minhui;Lo,Ying-Chu;Chiang,CharlestonWK
• 通讯作者: Chiang,CharlestonWK